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slicer2.py
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import numpy as np
# This function is obtained from librosa.
def get_rms(
y,
*,
frame_length=2048,
hop_length=512,
pad_mode="constant",
):
padding = (int(frame_length // 2), int(frame_length // 2))
y = np.pad(y, padding, mode=pad_mode)
axis = -1
# put our new within-frame axis at the end for now
out_strides = y.strides + tuple([y.strides[axis]])
# Reduce the shape on the framing axis
x_shape_trimmed = list(y.shape)
x_shape_trimmed[axis] -= frame_length - 1
out_shape = tuple(x_shape_trimmed) + tuple([frame_length])
xw = np.lib.stride_tricks.as_strided(
y, shape=out_shape, strides=out_strides
)
if axis < 0:
target_axis = axis - 1
else:
target_axis = axis + 1
xw = np.moveaxis(xw, -1, target_axis)
# Downsample along the target axis
slices = [slice(None)] * xw.ndim
slices[axis] = slice(0, None, hop_length)
x = xw[tuple(slices)]
# Calculate power
power = np.mean(np.abs(x) ** 2, axis=-2, keepdims=True)
return np.sqrt(power)
class Slicer:
def __init__(self,
sr: int,
threshold: float = -40.,
min_length: int = 5000,
min_interval: int = 300,
hop_size: int = 20,
max_sil_kept: int = 5000):
if not min_length >= min_interval >= hop_size:
raise ValueError('The following condition must be satisfied: min_length >= min_interval >= hop_size')
if not max_sil_kept >= hop_size:
raise ValueError('The following condition must be satisfied: max_sil_kept >= hop_size')
min_interval = sr * min_interval / 1000
self.threshold = 10 ** (threshold / 20.)
self.hop_size = round(sr * hop_size / 1000)
self.win_size = min(round(min_interval), 4 * self.hop_size)
self.min_length = round(sr * min_length / 1000 / self.hop_size)
self.min_interval = round(min_interval / self.hop_size)
self.max_sil_kept = round(sr * max_sil_kept / 1000 / self.hop_size)
def _apply_slice(self, waveform, begin, end):
if len(waveform.shape) > 1:
return waveform[:, begin * self.hop_size: min(waveform.shape[1], end * self.hop_size)]
else:
return waveform[begin * self.hop_size: min(waveform.shape[0], end * self.hop_size)]
# @timeit
def slice(self, waveform):
if len(waveform.shape) > 1:
samples = waveform.mean(axis=0)
else:
samples = waveform
if (samples.shape[0] + self.hop_size - 1) // self.hop_size <= self.min_length:
return [waveform]
rms_list = get_rms(y=samples, frame_length=self.win_size, hop_length=self.hop_size).squeeze(0)
sil_tags = []
silence_start = None
clip_start = 0
for i, rms in enumerate(rms_list):
# Keep looping while frame is silent.
if rms < self.threshold:
# Record start of silent frames.
if silence_start is None:
silence_start = i
continue
# Keep looping while frame is not silent and silence start has not been recorded.
if silence_start is None:
continue
# Clear recorded silence start if interval is not enough or clip is too short
is_leading_silence = silence_start == 0 and i > self.max_sil_kept
need_slice_middle = i - silence_start >= self.min_interval and i - clip_start >= self.min_length
if not is_leading_silence and not need_slice_middle:
silence_start = None
continue
# Need slicing. Record the range of silent frames to be removed.
if i - silence_start <= self.max_sil_kept:
pos = rms_list[silence_start: i + 1].argmin() + silence_start
if silence_start == 0:
sil_tags.append((0, pos))
else:
sil_tags.append((pos, pos))
clip_start = pos
elif i - silence_start <= self.max_sil_kept * 2:
pos = rms_list[i - self.max_sil_kept: silence_start + self.max_sil_kept + 1].argmin()
pos += i - self.max_sil_kept
pos_l = rms_list[silence_start: silence_start + self.max_sil_kept + 1].argmin() + silence_start
pos_r = rms_list[i - self.max_sil_kept: i + 1].argmin() + i - self.max_sil_kept
if silence_start == 0:
sil_tags.append((0, pos_r))
clip_start = pos_r
else:
sil_tags.append((min(pos_l, pos), max(pos_r, pos)))
clip_start = max(pos_r, pos)
else:
pos_l = rms_list[silence_start: silence_start + self.max_sil_kept + 1].argmin() + silence_start
pos_r = rms_list[i - self.max_sil_kept: i + 1].argmin() + i - self.max_sil_kept
if silence_start == 0:
sil_tags.append((0, pos_r))
else:
sil_tags.append((pos_l, pos_r))
clip_start = pos_r
silence_start = None
# Deal with trailing silence.
total_frames = rms_list.shape[0]
if silence_start is not None and total_frames - silence_start >= self.min_interval:
silence_end = min(total_frames, silence_start + self.max_sil_kept)
pos = rms_list[silence_start: silence_end + 1].argmin() + silence_start
sil_tags.append((pos, total_frames + 1))
# Apply and return slices.
if len(sil_tags) == 0:
return [waveform]
else:
chunks = []
if sil_tags[0][0] > 0:
chunks.append(self._apply_slice(waveform, 0, sil_tags[0][0]))
for i in range(len(sil_tags) - 1):
chunks.append(self._apply_slice(waveform, sil_tags[i][1], sil_tags[i + 1][0]))
if sil_tags[-1][1] < total_frames:
chunks.append(self._apply_slice(waveform, sil_tags[-1][1], total_frames))
return chunks, sil_tags
def main():
import os.path
from argparse import ArgumentParser
import librosa
import soundfile
parser = ArgumentParser()
parser.add_argument('audio', type=str, help='The audio to be sliced')
parser.add_argument('--out', type=str, help='Output directory of the sliced audio clips')
parser.add_argument('--db_thresh', type=float, required=False, default=-40,
help='The dB threshold for silence detection')
parser.add_argument('--min_length', type=int, required=False, default=5000,
help='The minimum milliseconds required for each sliced audio clip')
parser.add_argument('--min_interval', type=int, required=False, default=300,
help='The minimum milliseconds for a silence part to be sliced')
parser.add_argument('--hop_size', type=int, required=False, default=10,
help='Frame length in milliseconds')
parser.add_argument('--max_sil_kept', type=int, required=False, default=500,
help='The maximum silence length kept around the sliced clip, presented in milliseconds')
args = parser.parse_args()
out = args.out
if out is None:
out = os.path.dirname(os.path.abspath(args.audio))
audio, sr = librosa.load(args.audio, sr=None, mono=False)
slicer = Slicer(
sr=sr,
threshold=args.db_thresh,
min_length=args.min_length,
min_interval=args.min_interval,
hop_size=args.hop_size,
max_sil_kept=args.max_sil_kept
)
chunks = slicer.slice(audio)
if not os.path.exists(out):
os.makedirs(out)
for i, chunk in enumerate(chunks):
if len(chunk.shape) > 1:
chunk = chunk.T
soundfile.write(os.path.join(out, f'%s_%d.wav' % (os.path.basename(args.audio).rsplit('.', maxsplit=1)[0], i)), chunk, sr)
if __name__ == '__main__':
main()